AI Implementation & Integration
I build AI capabilities into products and internal workflows with the architecture, security, evaluation, and operational controls needed for production.
This is hands-on delivery, not a slide deck. I scope a concrete outcome, build the data and integration layers it needs, and harden the result for production. I also operate production AI systems myself, including BobSentry, an AI agent governance product.
What I deliver
- LLM & AI agent integration
- RAG pipelines over internal data
- MCP deployment & tool-use security
- Production hardening & evals
What I build and integrate
The engagement stays grounded in a useful workflow, an existing product, or a measurable internal capability rather than an open-ended AI experiment.
LLM & agent features
Product and workflow integrations for LLMs, structured outputs, tool use, and reliable human escalation points.
RAG over internal data
Retrieval pipelines over documents and operational data, including ingestion, retrieval tuning, and access boundaries.
MCP integrations
Model Context Protocol deployment and tool integration with clear permissions, auditability, and security boundaries.
Production hardening
Evaluation suites, monitoring, error handling, prompt and model controls, and operational documentation.
How the engagement works
Define the use case
I work with you to establish the workflow, data, success criteria, and constraints.
Design the system
I choose the architecture, model and data approach, integration points, and security controls.
Build & evaluate
I implement the capability and test it against real scenarios before production release.
Operate & improve
I document the system and establish the evaluation and operational practices needed to keep it dependable.